The array of sensors considered by the invention is made up of antennas that may not be collocated. The antennas exhibit reception patterns that may or may not be identical and their main axes are not oriented in the same direction in space. This type of antenna array is used notably in the field of goniometry.
One problem to be resolved in the field of the detection of electromagnetic signals lies in the absence of prior knowledge of the type of signal intercepted, in particular its frequency bandwidth, the type of modulation used or, more generally, any parameter associated with the wave form of the signal.
The known detection methods are generally constructed on the prior knowledge of the form of the signal and use a filter that is adapted according to this knowledge.
However, it is not possible to implement filters adapted to all the types of signals expected.
Two main types of receivers have been hitherto envisaged to perform a watch over a very wide band of frequencies: the receivers which permanently cover the band to be watched, which detect only the signals of high power, and the narrow band receivers, which do not make it possible to instantaneously cover the total band but which aim to detect signals of lower power and which allow for finer analyses of the signal.
The present invention falls within the scope of the narrow band receivers.
There is a problem to be resolved in devising a method which adapts to any type of signal, regardless of its bandwidth.
Furthermore, there is a problem specific to the arrays with pattern diversity because, at a given instant, the signal can be considered the same, over all the antenna elements of the array, only to within a complex gain, which cannot be reduced to a phase, dependent on the direction of arrival of the signal.
Furthermore, to create a detection processing procedure, it is important to minimize the transfers of data between the sensors and central member (which can be one of the sensors) in order to limit the complexity of the system. This means that it is preferable to implement most of the processing locally, which limits the use that can be made of the dependency (or correlation) of the measurements picked up at the same instant on two different sensors.
The traditional electromagnetic signal detection methods are notably based on the following preliminary steps.
The reception of signals is done through an antenna array with pattern diversity, or goniometric array, and the demodulation of the signal is performed by the same local oscillator for all the sensors of the array. The signal is then sampled, on each reception channel, in real or complex form, then one or more banks of filters are applied, for example, by weighted discrete Fourier transform. In other words, a number of temporarily overlapped discrete Fourier transforms are applied in order to produce an average adaptation to the band of the signals of interest. At the end of this operation, called time-frequency analysis, the signal is transformed into a time-frequency grid that is broken down into time-frequency cells, each cell containing the result of a discrete Fourier transform for a given time interval and a given frequency interval.
One known detection method consists in comparing the power of the signal, in each time-frequency cell, to a given detection threshold. However, this cell-by-cell decision-making is not optimal when the signal is spread in time and/or in frequency.
In effect, the hypothesis underpinning this method is that the signal is concentrated on a very small number of cells, in other words that the discrete Fourier transform used to perform the spectral analysis is adapted to the band of the signal. This hypothesis cannot be valid for all of the range of the signals to be processed, and no integration effect can compensate this defect. Furthermore, to ensure an average time between correct false alarms, since there are a lot of detectors in parallel, the detection threshold is high, making it necessary to have a strong signal-to-noise ratio.
One way of improving the abovementioned methods consists in performing an integration over sliding time-frequency windows, in order to concentrate the energy of the signals which are spread over a number of cells. This switch from a single-cell detector to a multi-cell detector makes it possible to benefit from an incoherent integration gain when the signal is spread over a number of time-frequency cells. The limitation on this method, however, lies in its single-channel nature and the match between the size of the window and the spread of the signal. If the window is underdimensioned, it does not take account of all the useful signal samples, and, conversely, if it is overdimensioned, it integrates noise samples and lowers the apparent signal-to-noise ratio.
Moreover, the known methods generally use only one antenna to perform the detection and consequently do not make use of all the information collected.